Overview

Dataset statistics

Number of variables15
Number of observations48842
Missing cells0
Missing cells (%)0.0%
Duplicate rows49
Duplicate rows (%)0.1%
Total size in memory3.0 MiB
Average record size in memory64.0 B

Variable types

Numeric12
Categorical3

Alerts

Dataset has 49 (0.1%) duplicate rowsDuplicates
relationship is highly correlated with genderHigh correlation
gender is highly correlated with relationshipHigh correlation
relationship is highly correlated with genderHigh correlation
gender is highly correlated with relationshipHigh correlation
relationship is highly correlated with genderHigh correlation
gender is highly correlated with relationshipHigh correlation
age is highly correlated with marital-statusHigh correlation
education is highly correlated with educational-numHigh correlation
educational-num is highly correlated with education and 1 other fieldsHigh correlation
marital-status is highly correlated with age and 1 other fieldsHigh correlation
occupation is highly correlated with educational-numHigh correlation
relationship is highly correlated with marital-status and 2 other fieldsHigh correlation
race is highly correlated with native-countryHigh correlation
gender is highly correlated with relationshipHigh correlation
native-country is highly correlated with raceHigh correlation
income is highly correlated with relationshipHigh correlation
workclass has 1432 (2.9%) zeros Zeros
education has 1389 (2.8%) zeros Zeros
marital-status has 6633 (13.6%) zeros Zeros
occupation has 5611 (11.5%) zeros Zeros
relationship has 19716 (40.4%) zeros Zeros
capital-gain has 44807 (91.7%) zeros Zeros
capital-loss has 46560 (95.3%) zeros Zeros

Reproduction

Analysis started2022-10-24 04:13:05.394080
Analysis finished2022-10-24 04:13:30.656850
Duration25.26 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.64358544
Minimum17
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2022-10-24T00:13:30.764599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile19
Q128
median37
Q348
95-th percentile63
Maximum90
Range73
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.71050993
Coefficient of variation (CV)0.35479394
Kurtosis-0.1842687406
Mean38.64358544
Median Absolute Deviation (MAD)10
Skewness0.5575803166
Sum1887430
Variance187.9780827
MonotonicityNot monotonic
2022-10-24T00:13:30.855303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361348
 
2.8%
351337
 
2.7%
331335
 
2.7%
231329
 
2.7%
311325
 
2.7%
341303
 
2.7%
371280
 
2.6%
281280
 
2.6%
301278
 
2.6%
381264
 
2.6%
Other values (64)35763
73.2%
ValueCountFrequency (%)
17595
1.2%
18862
1.8%
191053
2.2%
201113
2.3%
211096
2.2%
221178
2.4%
231329
2.7%
241206
2.5%
251195
2.4%
261153
2.4%
ValueCountFrequency (%)
9055
0.1%
892
 
< 0.1%
886
 
< 0.1%
873
 
< 0.1%
861
 
< 0.1%
855
 
< 0.1%
8413
 
< 0.1%
8311
 
< 0.1%
8215
 
< 0.1%
8137
0.1%

workclass
Real number (ℝ)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.870439376
Minimum-1
Maximum7
Zeros1432
Zeros (%)2.9%
Negative2799
Negative (%)5.7%
Memory size47.8 KiB
2022-10-24T00:13:30.946042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q13
median3
Q33
95-th percentile5
Maximum7
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.46423368
Coefficient of variation (CV)0.5101078575
Kurtosis1.64197141
Mean2.870439376
Median Absolute Deviation (MAD)0
Skewness-0.7479097326
Sum140198
Variance2.14398027
MonotonicityNot monotonic
2022-10-24T00:13:31.180303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
333906
69.4%
53862
 
7.9%
13136
 
6.4%
-12799
 
5.7%
61981
 
4.1%
41695
 
3.5%
01432
 
2.9%
721
 
< 0.1%
210
 
< 0.1%
ValueCountFrequency (%)
-12799
 
5.7%
01432
 
2.9%
13136
 
6.4%
210
 
< 0.1%
333906
69.4%
41695
 
3.5%
53862
 
7.9%
61981
 
4.1%
721
 
< 0.1%
ValueCountFrequency (%)
721
 
< 0.1%
61981
 
4.1%
53862
 
7.9%
41695
 
3.5%
333906
69.4%
210
 
< 0.1%
13136
 
6.4%
01432
 
2.9%
-12799
 
5.7%

fnlwgt
Real number (ℝ≥0)

Distinct28523
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189664.1346
Minimum12285
Maximum1490400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2022-10-24T00:13:31.474358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12285
5-th percentile39615.4
Q1117550.5
median178144.5
Q3237642
95-th percentile379481.65
Maximum1490400
Range1478115
Interquartile range (IQR)120091.5

Descriptive statistics

Standard deviation105604.0254
Coefficient of variation (CV)0.5567949135
Kurtosis6.057848212
Mean189664.1346
Median Absolute Deviation (MAD)60295.5
Skewness1.438891879
Sum9263575662
Variance1.115221019 × 1010
MonotonicityNot monotonic
2022-10-24T00:13:31.643568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20348821
 
< 0.1%
19029019
 
< 0.1%
12027719
 
< 0.1%
12589218
 
< 0.1%
12656918
 
< 0.1%
9918517
 
< 0.1%
12667517
 
< 0.1%
11336417
 
< 0.1%
18693416
 
< 0.1%
11156716
 
< 0.1%
Other values (28513)48664
99.6%
ValueCountFrequency (%)
122851
 
< 0.1%
134921
 
< 0.1%
137693
< 0.1%
138621
 
< 0.1%
148781
 
< 0.1%
188271
 
< 0.1%
192141
 
< 0.1%
193026
< 0.1%
193952
 
< 0.1%
194102
 
< 0.1%
ValueCountFrequency (%)
14904001
< 0.1%
14847051
< 0.1%
14554351
< 0.1%
13661201
< 0.1%
12683391
< 0.1%
12265831
< 0.1%
12105041
< 0.1%
11846221
< 0.1%
11613631
< 0.1%
11256131
< 0.1%

education
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.2884198
Minimum0
Maximum15
Zeros1389
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2022-10-24T00:13:31.793600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median11
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.874492433
Coefficient of variation (CV)0.3765877081
Kurtosis0.6765763119
Mean10.2884198
Median Absolute Deviation (MAD)2
Skewness-0.9362986762
Sum502507
Variance15.01169162
MonotonicityNot monotonic
2022-10-24T00:13:31.891308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1115784
32.3%
1510878
22.3%
98025
16.4%
122657
 
5.4%
82061
 
4.2%
11812
 
3.7%
71601
 
3.3%
01389
 
2.8%
5955
 
2.0%
14834
 
1.7%
Other values (6)2846
 
5.8%
ValueCountFrequency (%)
01389
 
2.8%
11812
 
3.7%
2657
 
1.3%
3247
 
0.5%
4509
 
1.0%
5955
 
2.0%
6756
 
1.5%
71601
 
3.3%
82061
 
4.2%
98025
16.4%
ValueCountFrequency (%)
1510878
22.3%
14834
 
1.7%
1383
 
0.2%
122657
 
5.4%
1115784
32.3%
10594
 
1.2%
98025
16.4%
82061
 
4.2%
71601
 
3.3%
6756
 
1.5%

educational-num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.07808853
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2022-10-24T00:13:31.985484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q312
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.570972756
Coefficient of variation (CV)0.2551051966
Kurtosis0.6257452728
Mean10.07808853
Median Absolute Deviation (MAD)1
Skewness-0.3165248567
Sum492234
Variance6.60990091
MonotonicityNot monotonic
2022-10-24T00:13:32.065371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
915784
32.3%
1010878
22.3%
138025
16.4%
142657
 
5.4%
112061
 
4.2%
71812
 
3.7%
121601
 
3.3%
61389
 
2.8%
4955
 
2.0%
15834
 
1.7%
Other values (6)2846
 
5.8%
ValueCountFrequency (%)
183
 
0.2%
2247
 
0.5%
3509
 
1.0%
4955
 
2.0%
5756
 
1.5%
61389
 
2.8%
71812
 
3.7%
8657
 
1.3%
915784
32.3%
1010878
22.3%
ValueCountFrequency (%)
16594
 
1.2%
15834
 
1.7%
142657
 
5.4%
138025
16.4%
121601
 
3.3%
112061
 
4.2%
1010878
22.3%
915784
32.3%
8657
 
1.3%
71812
 
3.7%

marital-status
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.618750256
Minimum0
Maximum6
Zeros6633
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2022-10-24T00:13:32.162440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.507702551
Coefficient of variation (CV)0.5757336146
Kurtosis-0.5361939917
Mean2.618750256
Median Absolute Deviation (MAD)2
Skewness-0.01632824007
Sum127905
Variance2.273166981
MonotonicityNot monotonic
2022-10-24T00:13:32.303659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
222379
45.8%
416117
33.0%
06633
 
13.6%
51530
 
3.1%
61518
 
3.1%
3628
 
1.3%
137
 
0.1%
ValueCountFrequency (%)
06633
 
13.6%
137
 
0.1%
222379
45.8%
3628
 
1.3%
416117
33.0%
51530
 
3.1%
61518
 
3.1%
ValueCountFrequency (%)
61518
 
3.1%
51530
 
3.1%
416117
33.0%
3628
 
1.3%
222379
45.8%
137
 
0.1%
06633
 
13.6%

occupation
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.577699521
Minimum-1
Maximum13
Zeros5611
Zeros (%)11.5%
Negative2809
Negative (%)5.8%
Memory size47.8 KiB
2022-10-24T00:13:32.565132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q12
median6
Q39
95-th percentile12
Maximum13
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.230509418
Coefficient of variation (CV)0.7584685051
Kurtosis-1.236280489
Mean5.577699521
Median Absolute Deviation (MAD)4
Skewness0.1105506005
Sum272426
Variance17.89720993
MonotonicityNot monotonic
2022-10-24T00:13:32.663423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
96172
12.6%
26112
12.5%
36086
12.5%
05611
11.5%
115504
11.3%
74923
10.1%
63022
6.2%
-12809
5.8%
132355
 
4.8%
52072
 
4.2%
Other values (5)4176
8.6%
ValueCountFrequency (%)
-12809
5.8%
05611
11.5%
115
 
< 0.1%
26112
12.5%
36086
12.5%
41490
 
3.1%
52072
 
4.2%
63022
6.2%
74923
10.1%
8242
 
0.5%
ValueCountFrequency (%)
132355
 
4.8%
121446
 
3.0%
115504
11.3%
10983
 
2.0%
96172
12.6%
8242
 
0.5%
74923
10.1%
63022
6.2%
52072
 
4.2%
41490
 
3.1%

relationship
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.443286516
Minimum0
Maximum5
Zeros19716
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2022-10-24T00:13:32.741907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.602151226
Coefficient of variation (CV)1.110071499
Kurtosis-0.7541163751
Mean1.443286516
Median Absolute Deviation (MAD)1
Skewness0.7917193051
Sum70493
Variance2.56688855
MonotonicityNot monotonic
2022-10-24T00:13:32.813988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
019716
40.4%
112583
25.8%
37581
 
15.5%
45125
 
10.5%
52331
 
4.8%
21506
 
3.1%
ValueCountFrequency (%)
019716
40.4%
112583
25.8%
21506
 
3.1%
37581
 
15.5%
45125
 
10.5%
52331
 
4.8%
ValueCountFrequency (%)
52331
 
4.8%
45125
 
10.5%
37581
 
15.5%
21506
 
3.1%
112583
25.8%
019716
40.4%

race
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
4
41762 
2
4685 
1
 
1519
0
 
470
3
 
406

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters48842
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
441762
85.5%
24685
 
9.6%
11519
 
3.1%
0470
 
1.0%
3406
 
0.8%

Length

2022-10-24T00:13:32.936020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-10-24T00:13:32.993407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
441762
85.5%
24685
 
9.6%
11519
 
3.1%
0470
 
1.0%
3406
 
0.8%

Most occurring characters

ValueCountFrequency (%)
441762
85.5%
24685
 
9.6%
11519
 
3.1%
0470
 
1.0%
3406
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
441762
85.5%
24685
 
9.6%
11519
 
3.1%
0470
 
1.0%
3406
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common48842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
441762
85.5%
24685
 
9.6%
11519
 
3.1%
0470
 
1.0%
3406
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII48842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
441762
85.5%
24685
 
9.6%
11519
 
3.1%
0470
 
1.0%
3406
 
0.8%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
1
32650 
0
16192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters48842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
132650
66.8%
016192
33.2%

Length

2022-10-24T00:13:33.056824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-10-24T00:13:33.113490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
132650
66.8%
016192
33.2%

Most occurring characters

ValueCountFrequency (%)
132650
66.8%
016192
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
132650
66.8%
016192
33.2%

Most occurring scripts

ValueCountFrequency (%)
Common48842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
132650
66.8%
016192
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII48842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132650
66.8%
016192
33.2%

capital-gain
Real number (ℝ≥0)

ZEROS

Distinct123
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1079.067626
Minimum0
Maximum99999
Zeros44807
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2022-10-24T00:13:33.184458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5013
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7452.019058
Coefficient of variation (CV)6.905979641
Kurtosis152.6930963
Mean1079.067626
Median Absolute Deviation (MAD)0
Skewness11.894659
Sum52703821
Variance55532588.04
MonotonicityNot monotonic
2022-10-24T00:13:33.269251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044807
91.7%
15024513
 
1.1%
7688410
 
0.8%
7298364
 
0.7%
99999244
 
0.5%
3103152
 
0.3%
5178146
 
0.3%
5013117
 
0.2%
4386108
 
0.2%
861482
 
0.2%
Other values (113)1899
 
3.9%
ValueCountFrequency (%)
044807
91.7%
1148
 
< 0.1%
4015
 
< 0.1%
59452
 
0.1%
91410
 
< 0.1%
9916
 
< 0.1%
105537
 
0.1%
10868
 
< 0.1%
11111
 
< 0.1%
115113
 
< 0.1%
ValueCountFrequency (%)
99999244
0.5%
413103
 
< 0.1%
340956
 
< 0.1%
2782858
 
0.1%
2523614
 
< 0.1%
251246
 
< 0.1%
220401
 
< 0.1%
2005149
 
0.1%
184812
 
< 0.1%
158318
 
< 0.1%

capital-loss
Real number (ℝ≥0)

ZEROS

Distinct99
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.50231358
Minimum0
Maximum4356
Zeros46560
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2022-10-24T00:13:33.366513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4356
Range4356
Interquartile range (IQR)0

Descriptive statistics

Standard deviation403.0045521
Coefficient of variation (CV)4.605644532
Kurtosis20.01434595
Mean87.50231358
Median Absolute Deviation (MAD)0
Skewness4.569808858
Sum4273788
Variance162412.669
MonotonicityNot monotonic
2022-10-24T00:13:33.458942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046560
95.3%
1902304
 
0.6%
1977253
 
0.5%
1887233
 
0.5%
241572
 
0.1%
148571
 
0.1%
184867
 
0.1%
159062
 
0.1%
160262
 
0.1%
187659
 
0.1%
Other values (89)1099
 
2.3%
ValueCountFrequency (%)
046560
95.3%
1551
 
< 0.1%
2135
 
< 0.1%
3235
 
< 0.1%
4193
 
< 0.1%
62517
 
< 0.1%
6534
 
< 0.1%
8102
 
< 0.1%
8806
 
< 0.1%
9742
 
< 0.1%
ValueCountFrequency (%)
43563
 
< 0.1%
39002
 
< 0.1%
37704
 
< 0.1%
36832
 
< 0.1%
31752
 
< 0.1%
30045
 
< 0.1%
282414
< 0.1%
27542
 
< 0.1%
26037
< 0.1%
255917
< 0.1%

hours-per-week
Real number (ℝ≥0)

Distinct96
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.42238238
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2022-10-24T00:13:33.553864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.05
Q140
median40
Q345
95-th percentile60
Maximum99
Range98
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.39144402
Coefficient of variation (CV)0.3065490774
Kurtosis2.95105909
Mean40.42238238
Median Absolute Deviation (MAD)3
Skewness0.2387496572
Sum1974310
Variance153.547885
MonotonicityNot monotonic
2022-10-24T00:13:33.643097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4022803
46.7%
504246
 
8.7%
452717
 
5.6%
602177
 
4.5%
351937
 
4.0%
201862
 
3.8%
301700
 
3.5%
551051
 
2.2%
25958
 
2.0%
48770
 
1.6%
Other values (86)8621
 
17.7%
ValueCountFrequency (%)
127
 
0.1%
253
 
0.1%
359
 
0.1%
484
 
0.2%
595
 
0.2%
692
 
0.2%
745
 
0.1%
8218
0.4%
927
 
0.1%
10425
0.9%
ValueCountFrequency (%)
99137
0.3%
9814
 
< 0.1%
972
 
< 0.1%
969
 
< 0.1%
952
 
< 0.1%
941
 
< 0.1%
923
 
< 0.1%
913
 
< 0.1%
9042
 
0.1%
893
 
< 0.1%

native-country
Real number (ℝ)

HIGH CORRELATION

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.74935506
Minimum-1
Maximum40
Zeros28
Zeros (%)0.1%
Negative857
Negative (%)1.8%
Memory size47.8 KiB
2022-10-24T00:13:33.739956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile18
Q138
median38
Q338
95-th percentile38
Maximum40
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.775343161
Coefficient of variation (CV)0.2174960401
Kurtosis12.77229318
Mean35.74935506
Median Absolute Deviation (MAD)0
Skewness-3.689528549
Sum1746070
Variance60.45596128
MonotonicityNot monotonic
2022-10-24T00:13:33.831888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3843832
89.7%
25951
 
1.9%
-1857
 
1.8%
29295
 
0.6%
10206
 
0.4%
32184
 
0.4%
1182
 
0.4%
7155
 
0.3%
18151
 
0.3%
4138
 
0.3%
Other values (32)1891
 
3.9%
ValueCountFrequency (%)
-1857
1.8%
028
 
0.1%
1182
 
0.4%
2122
 
0.2%
385
 
0.2%
4138
 
0.3%
5103
 
0.2%
645
 
0.1%
7155
 
0.3%
8127
 
0.3%
ValueCountFrequency (%)
4023
 
< 0.1%
3986
 
0.2%
3843832
89.7%
3727
 
0.1%
3630
 
0.1%
3565
 
0.1%
34115
 
0.2%
3321
 
< 0.1%
32184
 
0.4%
3167
 
0.1%

income
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
0
37155 
1
11687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters48842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
037155
76.1%
111687
 
23.9%

Length

2022-10-24T00:13:33.912677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-10-24T00:13:33.956686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
037155
76.1%
111687
 
23.9%

Most occurring characters

ValueCountFrequency (%)
037155
76.1%
111687
 
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037155
76.1%
111687
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Common48842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037155
76.1%
111687
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII48842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037155
76.1%
111687
 
23.9%

Interactions

2022-10-24T00:13:28.115993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.310717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:11.493398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.135370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.252081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.356440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.463302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.677275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.853491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.574238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.692354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.788519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.235165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.440537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.166836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.221150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.336066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.443595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.576763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.782840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.951321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.680740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.814838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.880330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.394758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.540484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.260892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.321389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.422894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.539439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.667638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.871946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:23.047500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.773498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.911773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.969169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.523062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.633207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.345271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.415419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.512026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.635349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.757258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.987830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:23.134464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.872012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.998330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.057385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.697986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.721654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.430814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.500968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.595051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.732262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.857705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.100062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:23.259134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.975500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.083037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.154470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.841281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.839402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.516899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.585336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.716315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.826109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.978703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.198094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:23.367246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.066886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.170512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.274604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.949741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:10.928402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.602265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.672506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.818670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.914044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.082835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.282994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:23.476459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.161833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.282224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.400234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:29.053096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:11.017447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.685555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.773828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.905653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.001039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.181522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.377326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:23.947544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.254274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.371581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.513234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:29.148096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:11.101525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.786329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.864863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.990100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.089002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.270927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.475415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.073790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.342462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.452046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.659165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:29.270154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:11.192889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.882260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.983478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.096318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.172137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.353514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.577141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.185326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.428079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.531376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.792470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:29.384403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:11.286235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:16.965363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.084290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.182689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.257043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.473160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.664753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.308454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.516433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.610306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:27.897194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:29.514625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:11.400863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:17.050552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:18.172239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:19.274444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:20.351863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:21.590731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:22.747584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:24.468714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:25.601557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:26.701573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-24T00:13:28.012649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-10-24T00:13:34.053935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-24T00:13:34.238147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-24T00:13:35.225424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-24T00:13:35.374453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-24T00:13:35.478735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-24T00:13:29.704132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-24T00:13:30.427171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ageworkclassfnlwgteducationeducational-nummarital-statusoccupationrelationshipracegendercapital-gaincapital-losshours-per-weeknative-countryincome
025322680217463210040380
138389814119240410050380
22813369517122100410040381
34431603231510260217688040381
418-110349715104-13400030380
534319869306471410030380
629-12270261194-14210040380
76351046261415290413103032381
82433696671510474400040380
955310499654220410010380

Last rows

ageworkclassfnlwgteducationeducational-nummarital-statusoccupationrelationshipracegendercapital-gaincapital-losshours-per-weeknative-countryincome
488323233406606250010040380
48833433846618112110410045380
4883432311613812144121110011350
488355333218651214230410040381
4883622331015215104101410040380
488372732573027122125400038380
48838403154374119260410040381
48839583151910119604400040380
48840223201490119403410020380
488415242879271192354015024040381

Duplicate rows

Most frequently occurring

ageworkclassfnlwgteducationeducational-nummarital-statusoccupationrelationshipracegendercapital-gaincapital-losshours-per-weeknative-countryincome# duplicates
122132433681314414100502503
23253195994324814000401203
242533081449134214100402503
01731530212841134000203802
1184378036284434100103802
219-116742815104-134100403802
3193972611194414100403802
4193130431434414100362502
519313815315104034000103802
6193139466151041134000253802